Transcription of INTRODUCTION MACHINE LEARNING - Stanford University
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INTRODUCTION MACHINE LEARNING - Stanford University
INTRODUCTION . TO. MACHINE LEARNING . AN EARLY DRAFT OF A PROPOSED. TEXTBOOK. Nils J. Nilsson Robotics Laboratory Department of Computer Science Stanford University Stanford , CA 94305. e-mail: November 3, 1998. Copyright 2005. c Nils J. Nilsson This material may not be copied, reproduced, or distributed without the written permission of the copyright holder. ii Contents 1 Preliminaries 1. INTRODUCTION .. 1. What is MACHINE LEARNING ? .. 1. Wellsprings of MACHINE LEARNING .. 3. Varieties of MACHINE LEARNING .. 4. LEARNING Input-Output Functions .. 5. Types of LEARNING .. 5. Input Vectors .. 7. Outputs .. 8. Training Regimes .. 8. Noise .. 9. Performance Evaluation .. 9. LEARNING Requires Bias .. 9. Sample Applications .. 11. Sources .. 13. Bibliographical and Historical Remarks .. 13. 2 Boolean Functions 15. Representation .. 15.
Chapter 1 Preliminaries 1.1 Introduction 1.1.1 What is Machine Learning? Learning, like intelligence, covers such a broad range of processes that it is dif-
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